Denoising of iodine-131 images using a median filter

Pandey, Anil Kumar; Sharma, Anubhuti; Sharma, Param Dev; Saroha, Kartik; Parida, Girish Kumar; Bal, Chandra Sekhar; Kumar, Rakesh

doi:10.1097/mnm.0000000000000970

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Although not directly related to our study; studies have been conducted for reducing noise levels in planar [2,3,35–37], SPECT [4,38–42] and PET images [43,44]. These studies do not involve neural networks for noise reduction, however.…”

Section: Discussionmentioning

confidence: 96%

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Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Chaudhary

Phulia

Pandey

et al. 2023

Nuclear Medicine Communications

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Introduction A DnCNN for image denoising trained with natural images is available in MATLAB. For Tc-99m DMSA images, any loss of clinical details during the denoising process will have serious consequences since denoised image is to be used for diagnosis. The objective of the study was to find whether this pre-trained DnCNN can be used for denoising Tc-99m DMSA images and compare its performance with block matching 3D (BM3D) filter. Materials and methods Two hundred forty-two Tc-99m DMSA images were denoised using BM3D filter (at sigma = 5, 10, 15, 20, and 25) and DnCNN. The original and denoised images were reviewed by two nuclear medicine physicians and also assessed objectively using the image quality metrics: SSIM, FSIM, MultiSSIM, PIQE, Blur, GCF, and Brightness. Wilcoxon signed-rank test was applied to find the statistically significant difference between the value of image quality metrics of the denoised images and the corresponding original images. Results Nuclear medicine physicians observed no loss of clinical information in DnCNN denoised image and superior image quality compared to its original and BM3D denoised images. Edges/boundaries of the scar were found to be well preserved, and doubtful scar became obvious in the denoised image. Objective assessment also showed that the quality of DnCNN denoised images was significantly better than that of original images at P-value <0.0001. Conclusion The pre-trained DnCNN available with MATLAB Deep Learning Toolbox can be used for denoising Tc-99m DMSA images, and the performance of DnCNN was found to be superior in comparison with BM3D filter.

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Section: Discussionmentioning

confidence: 96%

“…Nuclear medicine (NM) images are often corrupted with one or more types of noise -Gaussian noise, Poisson noise, mixture of Gaussian and Poisson noise, impulse noise, structured noise, salt and pepper noise, etc [1,2]. Filtering operations can be performed in either spatial domain or in frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Chaudhary

Phulia

Pandey

et al. 2023

Nuclear Medicine Communications

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“…Pollnow et al [25] pointed out in their study that Gaussian filter is a suitable tool for studying fluorescence optical data with low SNR. Pandey et al [26] proved that median filter improved image quality. Wiharto and Suryani 6…”

Section: Discussionmentioning

confidence: 99%

CT Image under Improved Fuzzy C-Means Clustering Algorithm for Evaluation of the Relationship between Cerebrospinal Fluid Change and Communicating Hydrocephalus after Decompressive Craniectomy in Patients with Traumatic Brain Injury

Zhang

Wang

2022

Scientific Programming

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This study was aimed to realize the automatic segmentation of communicating hydrocephalus lesions in brain CT after decompressive craniotomy (DC) in patients with traumatic brain injury (TBI) and discover correlation between cerebrospinal fluid changes and communicating hydrocephalus. Based on the traditional fuzzy C-means (FCM) algorithm, a new segmentation method filter-based FCM (FBFCM) algorithm was proposed. With 56 TBI patients as the research objects, the hydrocephalus lesions in CT images of patients after DC were segmented. The segmentation success rate (SSR), the segmentation coefficient Epc, the segmentation entropy Epe, and the number of iterations were indicators reflecting segmentation performance of FBFCM. The region of interest (ROI) on the segmented image was used to study the patient’s cerebral aqueduct, foramen magnum, and C2 level of the cerebrospinal fluid velocity and flow, to analyze the characteristics of the occurrence of communicative hydrocephalus. It was indicated that average Epc of FBFCM algorithm was 0.9321 ± 0.0144, Epe was 0.1126 ± 0.0081, the average number of iterations was 14.42 ± 3.79, and the segmentation success rate was 96%. Moreover, the above four indicators had statistically considerable differences compared with those of the FCM algorithm and the hard clustering algorithm (HCM) ( P < 0.05). Analysis of the cerebrospinal fluid flow rate in patients with communicative hydrocephalus found that the cerebrospinal fluid flow rate at the midbrain aqueduct of the patient increased greatly. The net flow was 0.000 ± 0.004 mL/s in the aqueduct of the midbrain, 0.001 ± 0.006 mL/s in the foramen magnum, and 0.002 ± 0.004 mL/s in the C2 layer. In summary, the FBFCM algorithm is effective in the segmentation and processing of CT images, which can further improve the effect of this diagnosis. After examination, it is concluded that the cranial cerebrospinal fluid flow rate and flow of TBI patients are improved after DC treatment, so that the patient’s condition can be effectively relieved. It has promotion value with clinical application.

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“…Pandey et al . [ 19 ] performed denoising of I-131 images using median filter; in this study, they have compared the performance of a plus-shape and a square-shape median filter for I-131 whole-body images. The plus-shape median filter was found to show better performance in comparison with the square-shape median filter.…”

Section: Discussionmentioning

confidence: 99%

Designing and Comparing Performances of Image Processing Pipeline for Enhancement of I-131-metaiodobenzylguanidine Images

Pandey

Dhiman

Arunraj

et al. 2021

Indian Journal of Nuclear Medicine

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Objective: An image processing pipeline can have more than one image processing technique in sequence, and the output of the first technique becomes input for the next technique and so on. In this study, we have designed and compared the performances of image processing pipelines for enhancement of I-131-metaiodobenzylguanidine (mIBG) images. Materials and Methods: Five different image processing pipelines (A [Gaussian filter, normalization], B [histogram specification (image 1), Gaussian filter, normalization], C [histogram specification (image 2), Gaussian filter, normalization], D [{histogram specification (image 3), Gaussian filter, and normalization], and E [histogram specification (image 4), Gaussian filter, normalization]) were designed and their performances were evaluated on I-131-mIBG images ( n = 122). The image quality was assessed objectively using Perception-based Image Quality Evaluator (PIQE) score and subjectively (on scale 1–4) by two nuclear medicine physician. Sign test was applied to find the statistically significant difference between the image quality obtained using image processing pipelines. We applied test of proportion to compute difference in proportion of image quality score assigned to images obtained using image processing pipelines. Results: Based on PIQE score, the quality of images obtained using all the five image processing pipelines were significantly better than that of input images ( P < 0.001). The highest image quality score (=4) was assigned maximum number of times ( n = 90) to the images obtained using image processing pipeline D and was significantly different from that of the second best image processing pipeline E ( P = 0.015). Conclusions: The image processing pipeline D was found to be better for enhancement of I-131-mIBG images.

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Denoising of iodine-131 images using a median filter

Cited by 5 publications

References 12 publications

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter

CT Image under Improved Fuzzy C-Means Clustering Algorithm for Evaluation of the Relationship between Cerebrospinal Fluid Change and Communicating Hydrocephalus after Decompressive Craniectomy in Patients with Traumatic Brain Injury

Designing and Comparing Performances of Image Processing Pipeline for Enhancement of I-131-metaiodobenzylguanidine Images

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